title: Novel Spiking Neuron-Astrocyte Networks based on nonlinear transistor-like models of tripartite synapses.
abstract: In this paper a novel and efficient computational implementation of a Spiking Neuron-Astrocyte Network (SNAN) is reported. Neurons are modeled according to the Izhikevich formulation and the neuron-astrocyte interactions are intended as tripartite synapsis and modeled with the previously proposed nonlinear transistor-like model. Concerning the learning rules, the original spike-timing dependent plasticity is used for the neural part of the SNAN whereas an ad-hoc rule is proposed for the astrocyte part. SNAN performances are compared with a standard spiking neural network (SNN) and evaluated using the polychronization concept, i.e., number of co-existing groups that spontaneously generate patterns of polychronous activity. The astrocyte-neuron ratio is the biologically inspired value of 1.5. The proposed SNAN shows higher number of polychronous groups than SNN, remarkably achieved for the whole duration of simulation (24 hours).
authors.forename: Gaetano
authors.initials: G
authors.lastname: Valenza
authors.forename: Luciano
authors.initials: L
authors.lastname: Tedesco
authors.forename: Antonio
authors.initials: A
authors.lastname: Lanata
authors.forename: Danilo
authors.initials: D
authors.lastname: De Rossi
authors.forename: Enzo Pasquale
authors.initials: EP
authors.lastname: Scilingo
mesh_terms: D000200:Action Potentials; D001253:Astrocytes / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission